Top 10 Reasons Why You Need Data Products: A Practitioner's Guide to Unlocking Data's True Potential

Originally published on

, the following is a revised edition.

Product management gave software its edge. It introduced discipline around defining value, aligning stakeholders, and iterating fast. Teams ship with purpose because there’s clarity on who the product is for and how success is measured.

Data needs that same energy. When data is treated like a product with defined users, feedback loops, and outcomes, it stops being a backlog of tickets and starts becoming a lever for impact. The shift is subtle, but the payoff is compounding.

Data Products in Today's Data Landscape: Born of Needs

Let's be honest. For many of us working with data, the daily grind often feels like a never-ending chase. Dashboards suddenly go blank, key reports show alien numbers, and figuring out “why” can feel like finding a needle in a haystack.

This isn't just multiple points of frustration; it is a deep-seated problem in how we handle data. Our usual style of working with data typically might include relying on quick fixes, patching data silos and pipelines that often break. But let’s admit that this approach can't keep up with what businesses need today.

The above patchwork or approach has consequences. Data that is hard to trust, reactions are slow, and the dream of being "data-driven" stays a dream. That brings us to the question: Why do we need Data Products?

Let’s break it down with a fitting analogy…

Imagine, for a moment, that your entire company's data strategy is like trying to build a castle out of a giant box of mixed-up LEGO bricks. In the old world, all you'd get is a mountain of unsorted pieces that includes a pile of red squares, a green triangle here, a blue rectangle there, without any blueprint or instruction manual.

Every time we wanted to build a tower (an insight, or a report in this case), we'd have to dig through the same piled-up box, hoping to find the right shapes, and then assemble them from scratch, often with a different, unstable method each time.

Does it work? Yes. Is it efficient? No.

The result? A lot of frustrations, wasted hours, and a tower that’s crumbled. If you relate to this, data products are relevant for you. Trust me, Data Products are indeed like getting that LEGO castle kit with all the pieces clearly sorted, labelled, and diligently organised into bags for each section, and with a clear instruction manual.

🎯 What Are Data Products?

Demystifying the Concept

Put simply, the Data Products are a clean, reliable, and easy-to-find piece of data, managed just like any other product you might buy. It is not just a simple data table or a dashboard, it's a clear set of actionable outcomes for a specific lever of your business, designed for certain users, customised to their needs.

Think of it as a straightforward interface for data that comes with a defined structure, clear meanings, and expected quality. Data Products bundle together the actual data, information about that data (metadata), the code used to process it, and the infrastructure it runs on. This makes data easy to use, reusable, trustworthy, and truly helpful across your whole company.

Visualising 'The Data Product Quantum,' the diagram effectively illustrates the interplay of Code, Data & Metadata, and underlying Infrastructure in its formation. — The Anatomy of a Data Product | Source: Data-First Stack as an Enabler for Data Products

10 Reasons to Adopt Data Products in the Modern Data Landscape

‘Data Products’ is not just a fancy term that techies like to throw around. It represents a fundamental change in how businesses handle and get value from their most important assets.

These top 10 reasons are powerful and relevant to understand why using Data Products is now the best bet:

1. Purpose-Driven Data for Tangible ROI

Data Products are specifically built to help achieve clear and strategic business goals. This very elemental tie to concrete outcomes ensures that every data effort brings measurable value, showing a clear ROI for data teams and their now directed efforts. By making sure data projects directly support the big picture, Data Products turn data from something that costs dollars into a strong tool for company scalability.

2. Powering Enterprise AI at Scale

For AI models to work well beyond tests, they need a steady supply of high-quality and relevant data. Data Products provide exactly this by exposing specific business logic, which acts as rich context for AI to understand your business truly. This organised information helps AI models learn, adapt, and perform reliably across the whole company, turning AI ideas into real, impactful solutions that add an edge to the business.

3. Data Quality: From an Afterthought to an Embedded Standard

Previously, checking data quality was often performed only after something broke or a model failed. And this discovery was often made at the very endpoint where the analyst or end-user is consuming the data. Data Products addresses this from its conception by knitting quality from the very beginning. It induces automatic checks, monitoring, and quality controls right where data is created, making sure it is accurate and consistent from day one and for every layer of data users (not just analysts, but also data engineers who are notified about quality issues early on in any of the upstream touchpoints). This means data quality isn't a problem to fix later, but a natural part of the data itself.

4. Federated Data Governance: Balancing Control with Agility

Managing data governance can feel like a balancing act between “what the local teams need” and “what the whole company requires.” Data Products support Federated Data Governance, which means it can follow both local rules and company-wide standards. Such federation (hybrid) creates room for role-, action-, or object-specific rules, ensuring responsible data handling while still letting teams move fast. This approach lets individual teams manage their data within a larger, independent, yet unified system.

5. Unlocking Self-Service Analytics for All: Wider Access to Data

Giving people power is key in today's data world. Data Products make self-served analytics a reality by empowering business users with simple ways to access and analyse data on their own. This significantly cuts down on the endless back-and-forth with IT or central data teams for quick, new questions. Users gain the freedom to find their own answers, building a company culture where everyone understands and uses data, & insights are discovered faster.

6. Accelerating Insights: Faster Time-to-Value

The requirements or the insights desired by end-users are where the Data Product journey begins: reverse engineering what data needs to be processed, how and whom to serve, and the business context within which to bind it. Compare this with traditional processes of spinning up ad-hoc dashboards with whatever data is available at the time. In the race to beat the competition, getting high-quality insights quickly is nothing less than a superpower. Well-built Data Products are designed to make things easy, hiding the complex bits underneath and giving clean, reliable information. This helps analysts, data scientists, and engineers work at an incredibly faster pace.

7. Composable & Reusable Across Teams

Think of Data Products as the "Lego bricks" of your data ecosystem. Much like APIs in software engineering, they are designed for composability and reusability, which means different teams can discover, integrate, and reuse existing Data Products or elements of it, reducing the time to reinvent the wheel and accelerating development. This unlocks more than scale, allowing organisations to build new and complex data applications without the labour usually associated with starting new use cases from scratch. Reusability of data, pipelines, and infrastructure is an incubator of innovation.

8. Fostering True Cross-Functional Collaboration

Data Products naturally create a strong environment for teamwork across different business areas. By exposing curated, trustworthy data available as clear products, it enables different functions to easily find and use each other's specific knowledge. This cuts down friction and iterations typically associated with cross-functional data sharing, enabling an easier way to shared understanding and a combined ability to solve complex business problems at pace.

9. High Cost Savings and Resource Optimisation

When code, infrastructure, and data are tightly coupled within Data Products, it provides exceptional clarity. This makes it much simpler to see which business goals or teams are using a lot of, say, computing power. Companies can then directly link these costs to how much value those business efforts generate. This helps in making smart decisions around resource allocation, focusing on the efforts that work best & cutting back on those that don't.

10. Future-proofing Data Strategy

In a tech world where the only constant is change, being able to adapt and do so quickly is key to long-term success. Data Products are built in a modular way, easy to find, and designed to evolve. This design principle makes the entire data system much robust and ready for constant changes in the business’s needs, advancing tech stacks, and often shifting team setups. Investment in Data Products isn’t just fixing the current day’s problems, but it is like building a flexible foundation that ensures data strategies either stay relevant or there's an innate ability of the infrastructure to easily adapt to new strategy paradigms.

Wrapping Up: Data Products Guide us from Silos to Collaboration and Transparency

Adopting Data Products goes beyond a technical upgrade, it is a paradigm shift in how a company fundamentally functions, thinks, and works together. It is about moving away from the messy ways we often handle data to an innovative, proactive approach where data is treated like a valuable product.

By using Data Products, organisations can break free from scattered data pieces, truly work together across different teams, and unlock the full power of their data. For data practitioners, this means less firefighting and more time building clever solutions that directly affect the business.

Want to Learn More About Data Products?

Check out The Data Product Playbook, which outlines the activation journey of Data Products in 6 weeks: From Data Product Design to Adoption and Scale.

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